Abstract: A tracking system for tracking objects within a field of view is disclosed. The field of view may include a first zone and an adjacent zone of interest where at least two gates are associated with respective sides of the first zone within the field of view. The first camera is configured to detect when an object crosses one of the at least two gates and track the object throughout the first zone and the zone of interest. The tracking system is configured to generate a first event message in response to the object being tracked from one of the gates into the zone of interest and subsequently leaving the first zone through a dedicated gate of the at least two gates.

Abstract: A method for machine-based training of a computer-implemented network for common detecting, tracking, and classifying of at least one object in a video image sequence having a plurality of successive individual images. A combined error may be determined during the training, which error results from the errors of the determining of the class identification vector, determining of the at least one identification vector, the determining of the specific bounding box regression, and the determining of the inter-frame regression.

Abstract: A tracking system for tracking objects within a field of view is disclosed. The field of view may include a first zone and an adjacent zone of interest where at least two gates are associated with respective sides of the first zone within the field of view. The first camera is configured to detect when an object crosses one of the at least two gates and track the object throughout the first zone and the zone of interest. The tracking system is configured to generate a first event message in response to the object being tracked from one of the gates into the zone of interest and subsequently leaving the first zone through a dedicated gate of the at least two gates.

Abstract: The presence of a stationary object is to be reliably recognized. Thereto, a presence detection device for detecting a presence of an object in its environment is provided, which comprises a movement detection unit for detecting an initial movement of the object in the environment of the presence detection device and for outputting a movement signal depending on the detection as well as a control unit for generating an activation signal depending on the movement signal. Moreover, the presence detection device comprises a camera, which can be activated by the activation signal, for obtaining a video signal of the environment of the presence detection device and an evaluation unit for generating a presence signal relating to the presence of the object by evaluating the video signal.

Abstract: The presence of a stationary object is to be reliably recognized. Thereto, a presence detection device for detecting a presence of an object in its environment is provided, which comprises a movement detection unit for detecting an initial movement of the object in the environment of the presence detection device and for outputting a movement signal depending on the detection as well as a control unit for generating an activation signal depending on the movement signal. Moreover, the presence detection device comprises a camera, which can be activated by the activation signal, for obtaining a video signal of the environment of the presence detection device and an evaluation unit for generating a presence signal relating to the presence of the object by evaluating the video signal.

Abstract: A training method for object recognition, the training method comprising: providing at least one top-view training image; aligning a training object present in the training image along a pre-set direction; labelling at least one training object from the at least one training image using a pre-defined labelling scheme; extracting at least one feature vector for describing the content of the at least one labelled training object and at least one feature vector for describing at least one background scene; and training a classifier model based on the extracted feature vectors.

Abstract: Fingerprint recognition and matching systems and methods are described that utilize features at all three fingerprint friction ridge detail levels, i.e., Level 1, Level 2 and Level 3, extracted from 1000 ppi fingerprint scans. Level 3 features, including but not limited to pore and ridge contour characteristics, were automatically extracted using various filters (e.g., Gabor filters, edge detector filters, and/or the like) and transforms (e.g., wavelet transforms) and were locally matched using various algorithms (e.g., the iterative closest point (ICP) algorithm). Because Level 3 features carry significant discriminatory and complementary information, there was a relative reduction of 20% in the equal error rate (EER) of the matching system when Level 3 features were employed in combination with Level 1 and Level 2 features, which were also automatically extracted. This significant performance gain was consistently observed across various quality fingerprint images.